R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(114
+ ,106.3
+ ,93.5
+ ,113.8
+ ,107.2
+ ,93.1
+ ,113.6
+ ,107.8
+ ,91
+ ,113.7
+ ,109.2
+ ,91.1
+ ,114.2
+ ,109.7
+ ,91.9
+ ,114.8
+ ,108.7
+ ,92.4
+ ,115.2
+ ,109.3
+ ,92.8
+ ,115.3
+ ,110.4
+ ,92.5
+ ,114.9
+ ,111.1
+ ,91.3
+ ,115.1
+ ,110.1
+ ,91.2
+ ,116
+ ,109.5
+ ,92.8
+ ,116
+ ,109
+ ,92.9
+ ,116
+ ,108.5
+ ,93
+ ,115.9
+ ,108.8
+ ,92.4
+ ,115.6
+ ,109.8
+ ,90.7
+ ,116.6
+ ,110.7
+ ,91.3
+ ,116.9
+ ,110.6
+ ,91.7
+ ,117.9
+ ,111.2
+ ,92.2
+ ,117.9
+ ,112
+ ,92.3
+ ,117.7
+ ,111.1
+ ,92.1
+ ,117.4
+ ,111.6
+ ,90.5
+ ,117.3
+ ,110.2
+ ,90.1
+ ,119
+ ,111.5
+ ,91.7
+ ,119.1
+ ,110.6
+ ,92.1
+ ,119
+ ,110.6
+ ,92.4
+ ,118.5
+ ,110.3
+ ,92.4
+ ,117
+ ,111.7
+ ,90
+ ,117.5
+ ,113.8
+ ,90.5
+ ,118.2
+ ,113.9
+ ,91.8
+ ,118.2
+ ,114.3
+ ,91.7
+ ,118.3
+ ,113.8
+ ,91.6
+ ,118.2
+ ,114.3
+ ,91.4
+ ,117.9
+ ,116.4
+ ,89.8
+ ,117.8
+ ,115.6
+ ,89.7
+ ,118.6
+ ,115.2
+ ,90.9
+ ,118.9
+ ,113.6
+ ,91
+ ,120.8
+ ,115.5
+ ,91.4
+ ,121.8
+ ,115.6
+ ,91.3
+ ,121.3
+ ,115.3
+ ,89.5
+ ,121.9
+ ,117.3
+ ,90.2
+ ,122
+ ,118.7
+ ,90.9
+ ,121.9
+ ,118.3
+ ,91.2
+ ,122
+ ,120.6
+ ,91.3
+ ,122.2
+ ,119.3
+ ,90.5
+ ,123
+ ,121.8
+ ,89.9
+ ,123.1
+ ,120.8
+ ,89.6
+ ,124.9
+ ,121.6
+ ,90.9
+ ,125.4
+ ,121.6
+ ,91.1
+ ,124.7
+ ,121.1
+ ,91.1
+ ,124.4
+ ,122.4
+ ,90.8
+ ,124
+ ,121.9
+ ,89.5
+ ,125
+ ,125.1
+ ,90.9
+ ,125.1
+ ,124.5
+ ,91.9
+ ,125.4
+ ,123.5
+ ,92.4
+ ,125.7
+ ,124.9
+ ,92.7
+ ,126.4
+ ,125.2
+ ,92.4
+ ,125.7
+ ,125.7
+ ,91.3
+ ,125.4
+ ,124.5
+ ,90.8
+ ,126.4
+ ,124.7
+ ,92.5
+ ,126.2
+ ,122.9
+ ,92.6)
+ ,dim=c(3
+ ,60)
+ ,dimnames=list(c('x'
+ ,'y'
+ ,'z')
+ ,1:60))
> y <- array(NA,dim=c(3,60),dimnames=list(c('x','y','z'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
x y z t
1 114.0 106.3 93.5 1
2 113.8 107.2 93.1 2
3 113.6 107.8 91.0 3
4 113.7 109.2 91.1 4
5 114.2 109.7 91.9 5
6 114.8 108.7 92.4 6
7 115.2 109.3 92.8 7
8 115.3 110.4 92.5 8
9 114.9 111.1 91.3 9
10 115.1 110.1 91.2 10
11 116.0 109.5 92.8 11
12 116.0 109.0 92.9 12
13 116.0 108.5 93.0 13
14 115.9 108.8 92.4 14
15 115.6 109.8 90.7 15
16 116.6 110.7 91.3 16
17 116.9 110.6 91.7 17
18 117.9 111.2 92.2 18
19 117.9 112.0 92.3 19
20 117.7 111.1 92.1 20
21 117.4 111.6 90.5 21
22 117.3 110.2 90.1 22
23 119.0 111.5 91.7 23
24 119.1 110.6 92.1 24
25 119.0 110.6 92.4 25
26 118.5 110.3 92.4 26
27 117.0 111.7 90.0 27
28 117.5 113.8 90.5 28
29 118.2 113.9 91.8 29
30 118.2 114.3 91.7 30
31 118.3 113.8 91.6 31
32 118.2 114.3 91.4 32
33 117.9 116.4 89.8 33
34 117.8 115.6 89.7 34
35 118.6 115.2 90.9 35
36 118.9 113.6 91.0 36
37 120.8 115.5 91.4 37
38 121.8 115.6 91.3 38
39 121.3 115.3 89.5 39
40 121.9 117.3 90.2 40
41 122.0 118.7 90.9 41
42 121.9 118.3 91.2 42
43 122.0 120.6 91.3 43
44 122.2 119.3 90.5 44
45 123.0 121.8 89.9 45
46 123.1 120.8 89.6 46
47 124.9 121.6 90.9 47
48 125.4 121.6 91.1 48
49 124.7 121.1 91.1 49
50 124.4 122.4 90.8 50
51 124.0 121.9 89.5 51
52 125.0 125.1 90.9 52
53 125.1 124.5 91.9 53
54 125.4 123.5 92.4 54
55 125.7 124.9 92.7 55
56 126.4 125.2 92.4 56
57 125.7 125.7 91.3 57
58 125.4 124.5 90.8 58
59 126.4 124.7 92.5 59
60 126.2 122.9 92.6 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) y z t
49.8478 0.1991 0.4567 0.1662
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.68008 -0.32904 0.03636 0.41425 1.75971
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 49.84776 11.42229 4.364 5.55e-05 ***
y 0.19910 0.06376 3.123 0.00283 **
z 0.45671 0.10830 4.217 9.12e-05 ***
t 0.16616 0.02142 7.759 1.93e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7834 on 56 degrees of freedom
Multiple R-squared: 0.9631, Adjusted R-squared: 0.9611
F-statistic: 486.6 on 3 and 56 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 3.330740e-03 6.661480e-03 0.9966692601
[2,] 4.201280e-04 8.402560e-04 0.9995798720
[3,] 1.736569e-04 3.473138e-04 0.9998263431
[4,] 9.682043e-05 1.936409e-04 0.9999031796
[5,] 1.563117e-05 3.126233e-05 0.9999843688
[6,] 7.852464e-06 1.570493e-05 0.9999921475
[7,] 5.638204e-06 1.127641e-05 0.9999943618
[8,] 2.155617e-06 4.311235e-06 0.9999978444
[9,] 3.847565e-07 7.695131e-07 0.9999996152
[10,] 5.717334e-07 1.143467e-06 0.9999994283
[11,] 2.574764e-07 5.149527e-07 0.9999997425
[12,] 3.125601e-06 6.251202e-06 0.9999968744
[13,] 1.004192e-06 2.008383e-06 0.9999989958
[14,] 2.698342e-07 5.396683e-07 0.9999997302
[15,] 8.809932e-08 1.761986e-07 0.9999999119
[16,] 3.648973e-08 7.297947e-08 0.9999999635
[17,] 5.897685e-07 1.179537e-06 0.9999994102
[18,] 1.477292e-06 2.954585e-06 0.9999985227
[19,] 1.665187e-06 3.330375e-06 0.9999983348
[20,] 1.142606e-05 2.285212e-05 0.9999885739
[21,] 5.806186e-04 1.161237e-03 0.9994193814
[22,] 4.831282e-03 9.662565e-03 0.9951687177
[23,] 1.253304e-02 2.506609e-02 0.9874669565
[24,] 1.830593e-02 3.661186e-02 0.9816940721
[25,] 2.057607e-02 4.115213e-02 0.9794239337
[26,] 2.571838e-02 5.143677e-02 0.9742816152
[27,] 3.816371e-02 7.632742e-02 0.9618362895
[28,] 1.230319e-01 2.460637e-01 0.8769681439
[29,] 2.626493e-01 5.252986e-01 0.7373507053
[30,] 4.984397e-01 9.968794e-01 0.5015602923
[31,] 5.452407e-01 9.095185e-01 0.4547592723
[32,] 6.808888e-01 6.382225e-01 0.3191112493
[33,] 7.468334e-01 5.063333e-01 0.2531666297
[34,] 7.884733e-01 4.230535e-01 0.2115267255
[35,] 7.622072e-01 4.755856e-01 0.2377927931
[36,] 7.394205e-01 5.211590e-01 0.2605794762
[37,] 8.848529e-01 2.302942e-01 0.1151470900
[38,] 9.748668e-01 5.026634e-02 0.0251331675
[39,] 9.846857e-01 3.062857e-02 0.0153142847
[40,] 9.916294e-01 1.674129e-02 0.0083706440
[41,] 9.905618e-01 1.887635e-02 0.0094381762
[42,] 9.993088e-01 1.382477e-03 0.0006912386
[43,] 9.993439e-01 1.312184e-03 0.0006560922
[44,] 9.976189e-01 4.762106e-03 0.0023810531
[45,] 9.949067e-01 1.018651e-02 0.0050932562
[46,] 9.882061e-01 2.358779e-02 0.0117938958
[47,] 9.573875e-01 8.522510e-02 0.0426125494
> postscript(file="/var/www/html/rcomp/tmp/1gyig1258647552.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ce6n1258647552.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3ljkr1258647552.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xr651258647552.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53q1r1258647552.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 60
Frequency = 1
1 2 3 4 5 6
0.11928080 -0.24338310 0.23009196 -0.16047741 -0.29155482 0.11303077
7 8 9 10 11 12
0.04472786 -0.10342710 -0.26090209 0.01771021 0.14027359 0.02799377
13 14 15 16 17 18
-0.08428604 -0.13614784 -0.02499716 0.35562776 0.32669468 0.81272065
19 20 21 22 23 24
0.44161114 0.34598458 0.51101402 0.70627959 1.25055342 1.18090015
25 26 27 28 29 30
0.77772821 0.17129956 -0.67749184 -0.99011551 -1.06990861 -1.27003598
31 32 33 34 35 36
-1.19097356 -1.46533978 -1.61886996 -1.68007761 -1.51464971 -1.10791979
37 38 39 40 41 42
0.06494761 0.92455017 1.14020166 0.85614572 0.19154964 -0.13198239
43 44 45 46 47 48
-0.70174154 -0.04370148 0.36641724 0.63637178 1.51720885 1.75970803
49 50 51 52 53 54
0.99309934 0.40512442 0.53224027 0.08956678 -0.31384313 -0.20925754
55 56 57 58 59 60
-0.49116914 0.11995571 -0.34337045 -0.34225372 -0.32464127 -0.37809139
> postscript(file="/var/www/html/rcomp/tmp/63ft71258647552.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 0.11928080 NA
1 -0.24338310 0.11928080
2 0.23009196 -0.24338310
3 -0.16047741 0.23009196
4 -0.29155482 -0.16047741
5 0.11303077 -0.29155482
6 0.04472786 0.11303077
7 -0.10342710 0.04472786
8 -0.26090209 -0.10342710
9 0.01771021 -0.26090209
10 0.14027359 0.01771021
11 0.02799377 0.14027359
12 -0.08428604 0.02799377
13 -0.13614784 -0.08428604
14 -0.02499716 -0.13614784
15 0.35562776 -0.02499716
16 0.32669468 0.35562776
17 0.81272065 0.32669468
18 0.44161114 0.81272065
19 0.34598458 0.44161114
20 0.51101402 0.34598458
21 0.70627959 0.51101402
22 1.25055342 0.70627959
23 1.18090015 1.25055342
24 0.77772821 1.18090015
25 0.17129956 0.77772821
26 -0.67749184 0.17129956
27 -0.99011551 -0.67749184
28 -1.06990861 -0.99011551
29 -1.27003598 -1.06990861
30 -1.19097356 -1.27003598
31 -1.46533978 -1.19097356
32 -1.61886996 -1.46533978
33 -1.68007761 -1.61886996
34 -1.51464971 -1.68007761
35 -1.10791979 -1.51464971
36 0.06494761 -1.10791979
37 0.92455017 0.06494761
38 1.14020166 0.92455017
39 0.85614572 1.14020166
40 0.19154964 0.85614572
41 -0.13198239 0.19154964
42 -0.70174154 -0.13198239
43 -0.04370148 -0.70174154
44 0.36641724 -0.04370148
45 0.63637178 0.36641724
46 1.51720885 0.63637178
47 1.75970803 1.51720885
48 0.99309934 1.75970803
49 0.40512442 0.99309934
50 0.53224027 0.40512442
51 0.08956678 0.53224027
52 -0.31384313 0.08956678
53 -0.20925754 -0.31384313
54 -0.49116914 -0.20925754
55 0.11995571 -0.49116914
56 -0.34337045 0.11995571
57 -0.34225372 -0.34337045
58 -0.32464127 -0.34225372
59 -0.37809139 -0.32464127
60 NA -0.37809139
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.24338310 0.11928080
[2,] 0.23009196 -0.24338310
[3,] -0.16047741 0.23009196
[4,] -0.29155482 -0.16047741
[5,] 0.11303077 -0.29155482
[6,] 0.04472786 0.11303077
[7,] -0.10342710 0.04472786
[8,] -0.26090209 -0.10342710
[9,] 0.01771021 -0.26090209
[10,] 0.14027359 0.01771021
[11,] 0.02799377 0.14027359
[12,] -0.08428604 0.02799377
[13,] -0.13614784 -0.08428604
[14,] -0.02499716 -0.13614784
[15,] 0.35562776 -0.02499716
[16,] 0.32669468 0.35562776
[17,] 0.81272065 0.32669468
[18,] 0.44161114 0.81272065
[19,] 0.34598458 0.44161114
[20,] 0.51101402 0.34598458
[21,] 0.70627959 0.51101402
[22,] 1.25055342 0.70627959
[23,] 1.18090015 1.25055342
[24,] 0.77772821 1.18090015
[25,] 0.17129956 0.77772821
[26,] -0.67749184 0.17129956
[27,] -0.99011551 -0.67749184
[28,] -1.06990861 -0.99011551
[29,] -1.27003598 -1.06990861
[30,] -1.19097356 -1.27003598
[31,] -1.46533978 -1.19097356
[32,] -1.61886996 -1.46533978
[33,] -1.68007761 -1.61886996
[34,] -1.51464971 -1.68007761
[35,] -1.10791979 -1.51464971
[36,] 0.06494761 -1.10791979
[37,] 0.92455017 0.06494761
[38,] 1.14020166 0.92455017
[39,] 0.85614572 1.14020166
[40,] 0.19154964 0.85614572
[41,] -0.13198239 0.19154964
[42,] -0.70174154 -0.13198239
[43,] -0.04370148 -0.70174154
[44,] 0.36641724 -0.04370148
[45,] 0.63637178 0.36641724
[46,] 1.51720885 0.63637178
[47,] 1.75970803 1.51720885
[48,] 0.99309934 1.75970803
[49,] 0.40512442 0.99309934
[50,] 0.53224027 0.40512442
[51,] 0.08956678 0.53224027
[52,] -0.31384313 0.08956678
[53,] -0.20925754 -0.31384313
[54,] -0.49116914 -0.20925754
[55,] 0.11995571 -0.49116914
[56,] -0.34337045 0.11995571
[57,] -0.34225372 -0.34337045
[58,] -0.32464127 -0.34225372
[59,] -0.37809139 -0.32464127
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.24338310 0.11928080
2 0.23009196 -0.24338310
3 -0.16047741 0.23009196
4 -0.29155482 -0.16047741
5 0.11303077 -0.29155482
6 0.04472786 0.11303077
7 -0.10342710 0.04472786
8 -0.26090209 -0.10342710
9 0.01771021 -0.26090209
10 0.14027359 0.01771021
11 0.02799377 0.14027359
12 -0.08428604 0.02799377
13 -0.13614784 -0.08428604
14 -0.02499716 -0.13614784
15 0.35562776 -0.02499716
16 0.32669468 0.35562776
17 0.81272065 0.32669468
18 0.44161114 0.81272065
19 0.34598458 0.44161114
20 0.51101402 0.34598458
21 0.70627959 0.51101402
22 1.25055342 0.70627959
23 1.18090015 1.25055342
24 0.77772821 1.18090015
25 0.17129956 0.77772821
26 -0.67749184 0.17129956
27 -0.99011551 -0.67749184
28 -1.06990861 -0.99011551
29 -1.27003598 -1.06990861
30 -1.19097356 -1.27003598
31 -1.46533978 -1.19097356
32 -1.61886996 -1.46533978
33 -1.68007761 -1.61886996
34 -1.51464971 -1.68007761
35 -1.10791979 -1.51464971
36 0.06494761 -1.10791979
37 0.92455017 0.06494761
38 1.14020166 0.92455017
39 0.85614572 1.14020166
40 0.19154964 0.85614572
41 -0.13198239 0.19154964
42 -0.70174154 -0.13198239
43 -0.04370148 -0.70174154
44 0.36641724 -0.04370148
45 0.63637178 0.36641724
46 1.51720885 0.63637178
47 1.75970803 1.51720885
48 0.99309934 1.75970803
49 0.40512442 0.99309934
50 0.53224027 0.40512442
51 0.08956678 0.53224027
52 -0.31384313 0.08956678
53 -0.20925754 -0.31384313
54 -0.49116914 -0.20925754
55 0.11995571 -0.49116914
56 -0.34337045 0.11995571
57 -0.34225372 -0.34337045
58 -0.32464127 -0.34225372
59 -0.37809139 -0.32464127
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7oync1258647552.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8k6ue1258647552.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9mxy21258647552.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10fkcv1258647552.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11e6ff1258647552.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12hpgd1258647552.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13u6ar1258647553.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/146dg21258647553.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15zmjs1258647553.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16qkat1258647553.tab")
+ }
>
> system("convert tmp/1gyig1258647552.ps tmp/1gyig1258647552.png")
> system("convert tmp/2ce6n1258647552.ps tmp/2ce6n1258647552.png")
> system("convert tmp/3ljkr1258647552.ps tmp/3ljkr1258647552.png")
> system("convert tmp/4xr651258647552.ps tmp/4xr651258647552.png")
> system("convert tmp/53q1r1258647552.ps tmp/53q1r1258647552.png")
> system("convert tmp/63ft71258647552.ps tmp/63ft71258647552.png")
> system("convert tmp/7oync1258647552.ps tmp/7oync1258647552.png")
> system("convert tmp/8k6ue1258647552.ps tmp/8k6ue1258647552.png")
> system("convert tmp/9mxy21258647552.ps tmp/9mxy21258647552.png")
> system("convert tmp/10fkcv1258647552.ps tmp/10fkcv1258647552.png")
>
>
> proc.time()
user system elapsed
2.448 1.531 2.869